CARE：置信度感知的回归估计用于建筑密度微调EO基础模型
CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models
AI总结：
提出CARE基础模型，通过在像素级回归任务中计算并分配置信度指标，并对低置信度区域执行自纠正学习，提升了EO数据预测的可靠性，在建筑密度估计上优于基线方法。
摘要：
在像素级回归任务中执行准确的置信度量化和评估，是地球观测（EO）AI基础模型的下游应用，这对于深度神经网络预测其失败、提升其性能并增强其在实际应用中的能力以实现实际部署具有重要意义。对于像素级回归任务，特别是利用EO基础模型中卫星影像的遥感数据时，置信度量化是一项关键挑战。本研究的重点是开发一个使用EO卫星数据的基础模型，该模型在回归输出之外计算并分配一个置信度指标，以提高深度神经网络生成预测的可靠性和可解释性。为此，我们开发、训练并评估了所提出的置信度感知回归估计（CARE）基础模型。我们的CARE模型计算并分配EO数据基础模型下游任务的回归结果置信度，并对低置信度区域执行置信度感知的自纠正学习方法。我们评估了CARE模型，在Copernicus Sentinel-2卫星星座的多光谱数据上估计建筑密度（即监测城市增长）的实验结果表明，所提出的方法可成功应用于EO和遥感中的重要回归问题。我们还表明，我们的CARE模型优于其他基线方法。
英文摘要：
Performing accurate confidence quantification and assessment in pixel-wise regression tasks, which are downstream applications of AI Foundation Models for Earth Observation (EO), is important for deep neural networks to predict their failures, improve their performance and enhance their capabilities in real-world applications, for their practical deployment. For pixel-wise regression tasks, specifically utilizing remote sensing data from satellite imagery in EO Foundation Models, confidence quantification is a critical challenge. The focus of this research work is on developing a Foundation Model using EO satellite data that computes and assigns a confidence metric alongside regression outputs to improve the reliability and interpretability of predictions generated by deep neural networks. To this end, we develop, train and evaluate the proposed Confidence-Aware Regression Estimation (CARE) Foundation Model. Our model CARE computes and assigns confidence to regression results as downstream tasks of a Foundation Model for EO data, and performs a confidence-aware self-corrective learning method for the low-confidence regions. We evaluate the model CARE, and experimental results on multi-spectral data from the Copernicus Sentinel-2 satellite constellation to estimate the building density (i.e. monitoring urban growth), show that the proposed method can be successfully applied to important regression problems in EO and remote sensing. We also show that our model CARE outperforms other baseline methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
